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Identifying the characteristics of and developing a predictive model for differentiating cancer-related
Khwanjira Archawanantakul1, Soamarat Vilaiyuk2, Samart Pakakasama3
1Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Background:
Musculoskeletal (MSK) symptoms are a frequent presentation in pediatric patients and may arise from a range of conditions, including juvenile idiopathic arthritis (JIA) and malignancies. Differentiating cancer-related MSK symptoms from JIA at initial presentation remains challenging due to overlapping clinical features. Delays in the diagnosis of malignancy can result in significant morbidity, underscoring the need for reliable diagnostic tools. The ONCOREUM score was developed to distinguish malignancies presenting with arthropathy from JIA and demonstrated high performance in initial validation. However, its utility is limited to patients with arthropathy and does not extend to other forms of MSK involvement. This study aimed to validate the ONCOREUM score in patients with arthropathy and to develop an expanded predictive model to distinguish cancer-related MSK symptoms from JIA.
Methods:
Patients aged < 16 years diagnosed with cancer or JIA were included. This retrospective study was conducted in two phases: (1) Evaluating the ability of the ONCOREUM score to identify cancer with arthropathy, (2) Developing a model to differentiate cancer-related MSK symptoms from JIA using stepwise logistic regression analysis.
Results:
A total of 1,026 patients were reviewed (646 cancer, 380 JIA). In phase 1, 26 cancer patients (4.0%) and 351 JIA patients (92.4%) were included. The ONCOREUM score (cutoff = - 6) had a sensitivity of 88.5% and specificity of 65.0%, with an AUC of 0.77. In phase 2, MSK symptoms were present in 84 (13%) cancer cases (61 hematologic, 23 solid tumors). The best-fitting model was obtained through multivariable analysis: back pain (OR 15.58, 95% CI 2.77-87.64, p < 0.02), nocturnal pain (OR 789.97, 95% CI 51.26-12,175.54, p < 0.0001), limb bone pain (OR 24.11, 95% CI 6.91-84.12, p < 0.0001), pallor (OR 6.30, 95% CI 1.55-25.60, p < 0.01), morning stiffness (OR 0.03, 95% CI 0.002-0.57, p < 0.02), additive arthritis (OR 0.003, 95% CI 0.00-0.04, p < 0.0001), and monoarticular involvement (OR 0.02, 95% CI 0.00-0.23, p < 0.002). This model yielded an AUC of 0.99 (95% CI 0.98-0.99).
Conclusions:
The refined predictive model is a promising clinical tool for differentiating cancer-associated MSK symptoms from JIA.
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